System and method for determination and adjustment of camera parameters using multi-gain images

ABSTRACT

This invention provides a system and method for auto-regulation of parameters a vision system camera and/or associated illumination of objects imaged by the camera using a plurality of differentiated gain (multi-gain) settings on the camera&#39;s image sensor to determine the gain value producing the most-readable image. The image (having the best characteristics) acquired using multiple gain settings can be read for information as a discrete gain image and/or the camera parameters (e.g. global gain and/or global exposure time) can be uniformly set across the pixel array to the best values for acquisition of a subsequent, higher sampled image. This image is then read (e.g. decoded) for information contained within any identified features of interest (e.g. found IDs).

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 61/716,370, filed Oct. 19, 2012, entitled SYSTEM AND METHOD FORDETERMINATION AND ADJUSTMENT OF CAMERA PARAMETERS USING MULTI-GAINIMAGES, the entire disclosure of which is herein incorporated byreference.

FIELD OF THE INVENTION

This invention relates to the control of parameters in vision systemcameras, and more particularly to auto-regulation of such parameters.

BACKGROUND OF THE INVENTION

Vision systems that perform measurement, inspection, alignment ofobjects and/or decoding of symbology codes or marks (e.g.one-dimensional (1D) and two-dimensional (2D) datamatrix bar codes,DotCodes, etc.—also termed “IDs”) are used in a wide range ofapplications and industries. These systems are based around the use ofan image sensor (also termed an “imager”), which acquires images(typically grayscale or color, and in one, two or three dimensions) ofthe subject or object, and processes these acquired images using anon-board or interconnected vision system processor. The processorgenerally includes both processing hardware and non-transitorycomputer-readable program instructions that perform one or more visionsystem processes to generate a desired output based upon the image'sprocessed information. This image information is typically providedwithin an array of image pixels each having various colors and/orintensities. In the example of an ID reader (also termed herein, a“camera”), the user or automated process acquires an image of an objectthat is believed to contain one or more barcodes, 2D codes or other IDtypes. The image is processed to identify code features, which are thendecoded by a decoding process and/or processor obtain the inherent datarepresented by the code.

A commonly used ID reader is the handheld type, that a user directs atan object or scene containing an ID, and then pull a trigger (typicallya button on the handle) to acquire and decode the code. Often, asuccessful reading/decoding of the code is followed by a visual and/oraudible alert—such as a green light and/or a beep. Handheld ID readerscan include one or more types of “internal” illumination—that is,illumination that is projected from the housing of the reader itself.Such illumination can be provided in a variety of colors, diffusivity,and/or angles with respect to the scene. More generally, the relativeangle at which an ID is imaged, the ambient light conditions and natureof the ID can all vary substantially. For example, IDs can be printed onan object as a high-contrast pattern or a low-contrast pattern, orformed on an object as a non-printed, peened/engraved pattern. Thereader should accommodate all these variations and make appropriateadjustment to its parameters, such as pixel gain, brightness exposureand/or illumination type and/or intensity so as to provide an optimalimage for decoding.

Prior art systems have attempted to optimize reader performance in avariety of manners. For example, some systems acquire a stream ofimages, each at different camera parameter settings, and analyze theimages to determine the quality of features in the image. One or more ofthe images is decoded. Other systems attempt to acquire pre-triggeredimages of the scene in an effort to determine prevailing conditions sothat, when the trigger is pulled, the image is acquired at a camerasetting that is more optimal to the conditions. However, the firstexemplary approach disadvantageously delays final acquisition of thedecoded image and the second exemplary approach disadvantageouslyrequires that the user maintain the reader in relatively the samelocation and orientation prior to pulling the trigger. Both of theseapproaches can reduce (or do not increase) the “snappiness” of thedevice—that is, the trigger-to-beep time, in which a successful readoccurs.

It is therefore desirable to provide a system and method that enablesincreased snappiness with respect to the recognition and handling (e.g.ID finding and decoding) of features of interest on object in imagedscenes where there can exist great variation between light and otherconditions with respect to each of the objects and/or each of thescenes. More generally, it is desirable that the system and methodeffectively provide for responsive auto-regulation of the device in avarying lighting and/or image-acquisition environments

SUMMARY OF THE INVENTION

This invention overcomes disadvantages of the prior art by providing asystem and method for auto-regulation of parameters a vision systemcamera and/or associated illumination of objects imaged by the camerausing a plurality of differentiated gain (multi-gain) settings on thecamera's image sensor to determine the gain value producing themost-readable gain image. The gain image (having the bestcharacteristics) acquired using multiple gain settings can be read forinformation as a discrete gain image (e.g. interleaved into an overallmultigrain image or extracted as a subsampled image) and/or the cameraparameters (e.g. global gain and/or global exposure time) can be setuniformly across pixels in the array to the best values for acquisitionof a subsequent, higher sampled image. This image is then read (e.g.decoded) for information contained within any identified features ofinterest (e.g. found IDs).

In an illustrative embodiment, a system and method for auto-regulationof settings in a vision system camera acquiring images of scenescontaining features of interest (e.g. IDs) is provided. A processor(CPU) receives image data from an image sensor (typically grayscale)having an array of pixels arranged in discrete pixel groups. These pixelgroups are each independently adjustable with a respective gain setting.An adjustment process reads the pixel groups and selects at least oneacquired gain image associated with at least one of the pixel groups.The selected gain image includes a version of a feature of interest inan imaged scene that allows information to be read therefrom withsufficient detail for the information to be used in a further process(e.g. decoding an ID in the image to generate decoded data).

When acquired, such gain images can reside within one overallinterleaved image that represents all or a portion of the availablefield of view of the sensor. Alternatively, gain images can be extractedfrom the interleaved image for subsequent analysis of these discretesubsampled images. The adjustment process illustratively changes atleast one parameter of the camera based upon a gain value of the pixelsassociated with the selected gain image, and this parameter is at leastone of gain and exposure. Illustratively, the processor can acquire andanalyze a further or subsequent image with pixels from a plurality, orall the pixel groups using the reset parameter(s). The parameter(s)is/are reset uniformly across some or all of the pixels in groups thatwere previously set at different gains so that these pixels are used toacquire the subsequent image. This reset includes a global reset of gainfor the pixels and a global reset of exposure time across the pixelarray. The processor can be constructed and arranged to (alternativelyor additionally) control a characteristic of an illuminator assemblybased upon the gain value. Such illumination characteristics can includebrightness, angle duration, etc. In an embodiment, each discrete pixelfrom each of the pixel groups can be organized into each of a pluralityof four-pixel (e.g. 2×2) matrices. These matrices are tessellated acrossthe pixel array. Alternatively, the pixel groups can each be organizedinto each of a plurality of tiles of pixels. The tiles are, likewise,tessellated across the pixel array.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention description below refers to the accompanying drawings, ofwhich:

FIG. 1 is a diagram of an exemplary handheld ID reader having an imagesensor and vision processor (CPU) configured to carry outauto-regulation functions based upon acquisition of multi-gain imagesaccording to an illustrative embodiment, shown acquiring images ofobjects in respective scenes having different orientations andcharacteristics;

FIG. 2 is a block diagram of the functional components of the ID readerof FIG. 1 showing the arrangement of components and passage ofinformation between components;

FIG. 3 is a diagram of a portion of an image sensor pixel arraycontaining tessellated groupings of 2×2 multi-gain pixels for use withthe system and method of FIG. 1;

FIG. 4 is a diagram of a portion of an image sensor pixel arraycontaining tessellated tiles of pixels, in which the gain of all pixelsin each tile can be independently set, for use with the system andmethod of FIG. 1;

FIG. 5 is a display of an exemplary overall multi-gain image comprisingfour interleaved gain images produced using the system and method ofFIG. 1;

FIG. 6 is a display of four discrete, subsampled gain images extractedfrom each set of discrete-gain pixels in the multi-gain image of FIG. 5,showing variation between the contrast of a background relative afeature of interest;

FIG. 7 is a graph showing a plot of luminance versus exposure for eachof the four discrete gain settings used to produce the multi-gain imageof FIG. 5; and

FIG. 8 is a flow diagram of a procedure for setting multi-gainpixels/tiles and determining a desirable illumination, gain and/orexposure for the image sensor for use in acquiring a subsequent image ofa scene with uniformly reset parameters.

DETAILED DESCRIPTION

FIG. 1 shows a vision system 100, which includes at least one ID reader110 that can be handheld, as shown, or fixed in a position with respectto an imaged scene. The reader can define any acceptable housing,including the depicted main body 112 and grip 114. In this embodiment,the reader includes a front window 116, which is surrounded by aninternal illumination system (illuminator). External illumination canoptionally be provided in synchronization with operation of the reader.The internal illuminator can comprise any arrangement and/or combinationof lighting elements. In this embodiment, and by way of example,discrete light elements (e.g. high-output LEDs) 120, 122 are employedand allow for differing color/wavelength, angle and/or intensity ofillumination. The illuminator can include conventional aiming LEDs (notshown) that project a beam onto a field of view to ensure that featuresof interest (e.g. barcodes or other types of IDs) are properly and fullyimaged. The reader 110 can include an indicator and interface panel 130,located at the rear of the body 112 in this embodiment. This panel caninclude on/off and other switches as well as lights to indicate a “good”or “failed” ID read (i.e. success or failure in reading/decoding theID). The grip 114 can include one or more trigger buttons 132 thattrigger illumination and image capture among other functions, such astoggling of aiming LEDs.

The reader 110 also includes one or more processing circuits, memory andthe like, that are collectively shown (in phantom) as a vision processor136 (also termed herein a CPU) and associated memory arrangement. Thisprocessor 136 performs various image processing, and image datahandling/storage functions. Illustratively, the processor 136 receivescaptured image frame data in the form of color or grayscale pixels(among other formats) from the image sensor (also shown in phantom). Theprocessor searches for ID features (or other features of interest) inthe image, and then passes appropriate data to a decoding process thatgenerates decoded data from the ID features. This decoded data is storedand/or passed via a communication link (which can be wired, or wirelessas shown) 140 to a receiver 142 that is interconnected via a network orother link with a data processing and storage system 144. This system144 can comprise a conventional server or PC running appropriateapplications for handling and storing decoded data transmitted from thereader 110. Such applications and the architecture of the system 144should be clear to those of skill in the art.

The reader 110 also includes a lens assembly 150 (shown in phantombehind window 116) that can be fixed-focus or auto-focusing. By way ofexample, an object O1 having an ID S1 is imaged by the reader 110 withthe lens 150 focusing upon a field of view FOV1 in which the ID S1occupies a particular location and orientation with respect to thereader. The focal distance D1 along optical axis OA1 is within anacceptable operating range. Likewise, the reader 110 can be focused (asshown in phantom) on another object O2 located at a differing locationand orientation (and focal distance D2) along optical axis OA2. The twoexemplary orientations can vary widely in illumination/ambient lightcharacteristics, ID characteristics (e.g. high-contrast, low-contrast,peened, black-on-white, white-on-black, ID size (e.g. 2 mils-20 mils insize), etc.), focal distance, and/or angle of the optical axis withrespect to the ID. These factors can all affect the snappiness of thesystem 100 reading from one ID to another. To the extent that factorssuch as pixel gain and exposure can be optimized to improve snappiness,the illustrative embodiment is adapted so that the vision processor/CPU136 and interconnected image sensor 138 operate to select an optimalpixel gain and/or exposure. These parameter settings allow for quickerand more-accurate reading/decoding of IDs or other features of interest.

With reference to FIG. 2, the system 100 and the interoperation ofcomponents therein is shown schematically. As depicted, the CPU isoperatively connected with a programmable memory arrangement 210 thatcan consist of one or more RAMs that store (for example) program data212, CPU operating instructions 214 and/or image data 216. This data 220is transferred to and from the CPU 136 via appropriate bus architecture.The sensor 138 transmits image data 218 to the memory 210 based onacquired image frames. The image data represents light 230 reflectedfrom an object O having IDs and/or other features of interest. Aninternal and/or external illuminator 240 along with any ambientillumination transmits light onto the object O, which is reflected(light 230) in a specific manner toward the sensor 138. The manner inwhich the light is reflected affects the ability of the sensor to findand decode IDs. Illumination timing, intensity, pattern, type, etc. canbe controlled by appropriate strobe signals 246 provided by the CPU 136.This acquired and stored image data 216 is processed as appropriate bythe CPU 136. This processing includes use of the image data 216 invarious auto-regulation functions in which camera parameters areadjusted (described further below). Such auto-regulation is provided inpart by configuration parameters 250 that are set (260) by the CPU 136.Illustratively, such parameters include exposure and gain. According toan illustrative embodiment, the parameters 250 are set based upon imagestriggered (262) by the CPU and used, at least in part, forauto-regulation processes.

It is recognized that a variety of commercially available image sensorscontain functionality that enables the gain of individual pixels in agrouping of pixels to be adjusted. More particularly, variouscommercially available sensors allow for each of four pixels in each 2×2grouping (across the entire sensor) to be adjusted in order to adjustcolor sensor response to Red/Green/Blue (RGB) a standard Bayer pattern.That is, where the sensor is capable of sensing color, each pixel iscovered with a certain color filter (termed a Bayer filter). The pixelsdefine a tessellated pattern of red, green and blue filters—often wheregreen appears twice in the group of four. Alternatively sensors canemploy cyan magenta yellow (CMY), or another set of wavelengths. Suchexemplary (CMOS) sensors include, but are not limited to, model MT9M001available from Micron Technology, Inc. of Boise, Id. and model EV76C560available from e2v Technologies of the United Kingdom. When the sensoris provided free of any color filter it operates as a grayscale unit.FIG. 3 depicts a portion of an exemplary sensor pixel array 310.Groupings (dashed boxes 320) of four pixels (2×2) are depicted. Eachgrouping (or matrix) allows for four independent gain values G1, G2, G3and G4 to be adjusted therein. That is, the location denoted G1 isadjusted in a tessellated manner across the entire pixel array.Likewise, G2, G3 and G4 can each be independently adjusted across thearray. All pixels denoted either G1, G2, G3 or G4 carry the sameadjusted gain value.

Other sensors allow for the independent adjustment of gain in individualtiles of pixels. For example, the model MT9V034 available from AptinaImaging Corporation of San Jose, Calif. allows for independentadjustment of a tile of pixels. As shown a series of 5×5 tiles (P5) of25 pixels can be independently set to a desired gain. By way of example,each tile in the sensor pixel array 410 can be set to one of four gainvalues G1, G2, G3 and G4, and all pixels in that tile carry the samegain value. The specific gain values in each of the tiles aretessellated across the entire sensor array as shown. In alternateembodiments, fewer or greater than four independently adjustable gainvalues can be provided to the arrangement of FIG. 3 or FIG. 4.

The adjustment of each gain value G1-G4 across the pixel array (310 or410) is accomplished in accordance with the specifications provided bythe manufacturer of the sensor based upon program instructions andprocesses carried out in the CPU. As used herein the terms “process”and/or “processor” should be taken broadly to include a variety ofelectronic hardware and/or software based functions and components.Moreover, a depicted process or processor can be combined with otherprocesses and/or processors or divided into various sub-processes orprocessors. Such sub-processes and/or sub-processors can be variouslycombined according to embodiments herein. Likewise, it is expresslycontemplated that any function, process and/or processor here herein canbe implemented using electronic hardware, software consisting of anon-transitory computer-readable medium of program instructions, or acombination of hardware and software.

By way of non-limiting example, FIG. 5 depicts a “multi-gain” displayedimage 510 of an object with a central feature of interest 512. Bysetting the individual gain of each of four pixel tiles 520, 522, 524and 526 in the exemplary array (e.g. array 410 in FIG. 4) of pixels(e.g. the array 310 of FIG. 3), the resulting image data appears as aseries of lighter or darker regions across the entire image field. Thisoverall image is essentially four interleaved gain images, each with onefourth the total pixel count of the array within the displayed area. Asshown in FIG. 6, each of four unique, partial (subsampled) gain images620, 622, 624 and 626 respectively correspond to all tiles (or pixels)520, 522, 524 and 526 in the multi-gain image 510. It should be clearthat the feature of interest 512 (a dark letter “C”) is most clear usingthe gain setting for pixels 522. Conversely, the feature 512 ispractically invisible using the gain of pixels 624. The images 626 and620 display intermediate visibility for the exemplary feature ofinterest 512.

Thus, by setting a series of low to high gains, one of the four images620, 622, 624 and 626 is generally in a range of gray levels in whichthe pixels are neither washed out (too light) due to over-exposure, orso dark that the image noise is greater than the signal. In illustrativeembodiments, the gain image (e.g. subsampled image 622) is sufficientthat acceptable information—such as a readable ID can be deriveddirectly from it. In other embodiments, the image whose luminance isclosest to the ideal (622) is used as the reference for predicting thebest gain and/or exposure for the normal full/high resolution(“resolution” meaning the pitch/frequency of the image) image, which canbe acquired in a subsequent image frame after the gain of all pixels isset uniformly to the gain that produces the most-readable image. In thedepicted example, gains are set to 1.5, 5, 10, and 15. However, thesefour gain values are exemplary of a wide range of possible values thatcan be applied to discrete groups of independently settable pixels.

Reference is made to FIG. 7, which depicts a graph 700 of Exposure(microseconds) versus Luminance (cd/M²) for each of the fourabove-described, exemplary gain values based upon measurements using theabove-described sensor arrangement (FIG. 4). Only the luminance valuesthat are less than 150 are plotted, in order to avoid the non-linearplateau caused by over-exposure, we get nice linear data for each gainas seen in the first chart below left. As shown, each graph line issubstantially linear across the range of values plotted. Thispredictability assists in determining the correct gain setting,particularly for images that exhibit optimal characteristics between twoof the gain settings.

FIG. 8 is a flow diagram of a procedure 800 for determining anappropriate gain setting for the pixels of the image sensor to obtain areadable image, and adjusting gain and/or exposure settings tosubsequently acquire a sufficiently high quality version of the image toobtain information (e.g. decoded ID data) therefrom. The procedure 800begins when the user or another automated process (e.g. an assembly lineencoder and/or object detection process) transmits a trigger signal,requesting image acquisition (step 810). In step 820, each of aplurality of gain settings are applied to each respective set of pixelsor pixel tiles across the image sensor. These values can be selectedinitially and in any subsequent iterations of the process (describedbelow) using a variety of techniques. In a general technique, the valuesare set across a wide range of available gain settings as describedabove.

Notably, the speed of acquisition of the multi-gain image is increasedby applying a minimum exposure time in conjunction with a full range ofgain settings. At this minimum exposure time a higher gain setting ismore likely to provide an acceptable image for the purpose ofdetermining and setting optimal camera parameters. That is, if a lowergain would provide an acceptable image at a longer exposure time, butnot at the chosen shortened time, a higher gain setting in one of thegain images provides the desirable image quality at the shorter time.The tradeoff is a high signal to noise ratio (SNR) in theshorter-exposure, higher-gain image. However, for the purpose ofdetermining the best set of camera parameters from the gain image foruse in acquiring a normal, longer exposure image (described below), ahigher noise content in the gain image does not typically affect theanalysis. In an embodiment, the exposure time for acquiring themulti-gain image can be approximately 1000 microseconds (1 millisecond).

Note, as described further below, that in extrapolating the parametersfor acquiring a next image, the processor attempts continually tocalibrate the black levels on the sensor. This can shift the pixelvalues by several steps and cause discrepancies between images andbetween neighboring pixels, since there are four different black levelsettings for each of the four pixels in the 2×2 multi-gain pixel matrixor tiles. Since the black level correction is typically applied beforethe Analog to Digital Conversion in readout of the sensor, the valuesfor the correction are typically valued as a voltage reading, and notdigital pixel intensity levels (e.g. 0-255), so a correction, is appliedto convert the prevailing sensed voltage into a pixel offset. Thisconversion is set at 50%, based on empirical measurements. The use ofblack level calibration values generally makes it possible to predictexposures that produce luminance values closer to the desired target,even over large exposure changes.

Based upon the settings, in step 830, the sensor then acquires the imageof a scene over a predetermined exposure time and transmits the imagedata to the CPU. The CPU and associated auto-regulation process (800)then determines (decision step 840) whether one or more of the resultingdiscrete gain images is sufficiently readable by system processes toresolve information in features of interest, or more generally one ormore of the images contains contrast and other characteristics thatrender it sufficient to obtain information (e.g. decoded ID data). Ifeach gain image is insufficient, then the CPU can determine whether anychange in gain would improve the image quality (decision step 842). At(or about) this time, the procedure 800 can obtain the current blacklevel reading from the sensor (step 841). This assists in determining ifit should be further adjusted. If the image is basically unreadable,even with four different gain settings, then the decision step 842directs the system to indicate a failed read (step 844). If animprovement in reading is possible with different gain settings, then,(optionally) the decision step 842 branches to step 850 and a new set ofgains is selected to be used in the acquisition of a subsequentmulti-gain image in step 820. These new gains can be determined byproviding a series of intermediate gain settings between those of one ormore pairs of gain images that appear to show possibly readable images.Alternatively a second set of pre-defined (non-dependent on the imagesof the first set) gain values can be employed—for example, if none ofthe first set of gain values provides a promising image. A variety ofother techniques can be employed for selecting new gain values in step850. The number of iterations in which gain is changed and readjusted ishighly variable. Likewise, it is expressly contemplated that the initialadjustment of gain can form the basis of a set of gain images that arethen used in other types of adjustment processes (other than iterativegain readjustment), with the goal of eventually adjusting cameraparameters (and/or illumination characteristics) to attain amore-readable image.

Note that it is contemplated that the multi-gain image need not beanalyzed over the entire sensor array, but over a reduced portion(and/or reduced field of view) from a predetermined region (e.g. thecentral region), or a portion that likely contains a feature, or aportion that is otherwise indicative of the general pattern or patternsresiding in the image. This focus on a region typically decreasesprocessing time.

When a new multi-gain image is acquired and analyzed via steps 830 and840, the procedure 800 then uses the gain settings in step 854 tocompute new camera parameters (e.g. global gain and global exposure) toapply uniformly over some or all of the pixels in the sensor) for use inthe acquisition of a subsequent image. The computation of new parameterscan be carried out using a variety of techniques that can rely, forexample on look-up tables and/or equations. These computations can relyin part on experimental data, such as, for example, that shown in thegraph of FIG. 7.

Note also that if the new gain settings generated in step 850 are for again image already deemed to be readable (but possibly below athreshold), the procedure 800 can branch (via dashed-line branch 852 inprocedure 800) to step 854, and use at least one of the new settings tofurther refine parameters in acquiring a subsequent image.

Once the gain image is considered readable and any new parameters havebeen computed, the procedure can provide that result to at least one ofsteps 860, 862 and/or 864 or to decision step 842. More generally, theprocedure 800 can include a maximum number of iterations (e.g. oneiteration of step 850) until the system indicates a failed read (step844) or transmits parameters and/or the best gain image to steps 860,862 and/or 864. These steps can be employed in combinations or invarious alternatives (or in alternate embodiments). In step 860, theprocedure 800 directs the system to vary the intensity, pattern and/ortype of illumination and acquire a subsequent image with this newillumination characteristic (and new uniformly set sensor parameters.The illumination characteristics can be based on a lookup or othercomputation that compares the gain and/or exposure with the best imageto an associated illumination parameters can be part of the computationmade in step 854. Some or all of this information can be generated usingtrial and error approaches that generate a table of values, or byempirical formulas. Alternatively (or in addition to step 860), theprocedure 800 can use the gain information to reset the global gainand/or exposure of the sensor to acquire a subsequent image usinguniformly set pixels across the sensor array. This subsequent image ismore likely to be readable. In general, the pixels used to acquire othergain images are reset to a new global gain setting, and a longerexposure time is employed in acquiring this subsequent image. A furtheralternative is that the acquired gain image can be processed directly(step 864) if its features provide sufficient detail to derive desiredinformation (e.g. a decodable ID). The parameter-computation step 854can be optional in such instances.

The processing of the acquired image from at least one of steps 860, 862and 864 occurs at step 870 in which information in the image is read,decoded, or otherwise analyzed, to generate desired data (e.g. analphanumeric and/or other data stream).

It has been experimentally observed that a multi-gain image (e.g. FIG.5) with a reduced field of view and subsampling can be acquired in onetenth the time of a normal image (4 ms versus 40 ms), and it can predictan acceptable gain/exposure for the subsequent image from both dark andbright ambient conditions.

It should be clear that the above-described system and method forauto-regulation of parameters of a vision system camera provides arelatively fast and accurate technique for adjusting gain and exposureto account for widely varying conditions between each runtime imageacquisition event. This system and method employs features inherent oncertain sensors and can be implemented with a minimum of added softwarecode and/or hardware. This system and method also allows for the use ofgain images (interleaved and/or extracted, subsampled images) to obtaininformation or iterative refinement of the camera configurationparameters as appropriate to obtain the desired image quality forsuccessful reading of image feature information.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments of the apparatus and method of the presentinvention, what has been described herein is merely illustrative of theapplication of the principles of the present invention. For example, itis expressly contemplated that the image sensor used herein can be anyacceptable model or type, generally including the ability todifferentiate the gain or other similar setting in individual pixels orgroups of pixels. Additionally, the processor arrangements used hereinare only exemplary of a variety of processor arrangements that can beinternal and/or external to the reader. In alternate embodiments, theCPU can be all, or in part, located external to the reader housing withimage data transferred over a link to the CPU for processing. Also,while the illustrative embodiment is exemplified by an ID reader(handheld or fixed-mount), it is expressly contemplated that other typesof vision systems that operate in wide dynamic ranges can benefit by theteachings herein, including robotic manipulators and surveillancesystems (e.g. systems with facial recognition). Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

What is claimed is:
 1. A system for auto-regulation of settings in avision system camera acquiring images of scenes containing features ofinterest comprising: a processor that receives image data from an imagesensor having an array of pixels arranged in discrete pixel groups, eachdiscrete pixel from each of the discrete pixel groups is organized intoeach of a plurality of matrices, the matrices being tessellated acrossthe array of pixels, that are each independently adjustable with arespective gain setting; and an adjustment process that reads the pixelgroups and selects at least one acquired gain image associated with atleast one of the pixel groups, the selected gain image including aversion of a feature of interest in an imaged scene that allowsinformation to be read therefrom.
 2. The system as set forth in claim 1wherein the adjustment process changes at least one parameter of thecamera applied uniformly to the array of pixels based upon a gain valueof the pixels associated with the selected gain image.
 3. The system asset forth in claim 2 wherein the parameter is at least one of a globalgain setting and a global exposure setting.
 4. The system as set forthin claim 2 wherein the processor is constructed and arranged to acquireand analyze a further image with pixels from a plurality of the pixelgroups.
 5. The system as set forth in claim 2 wherein the processor isconstructed and arranged to control a characteristic of an illuminatorassembly based upon the gain value.
 6. The system as set forth in claim1 wherein the plurality of matrices comprise a plurality of four-pixelmatrices.
 7. The system as set forth in claim 1 wherein the pixel groupsare each organized into each of a plurality of tiles of pixels, thetiles being tessellated across the pixel array.
 8. The system as setforth in claim 1 wherein the processor is constructed and arranged toread the information from the feature of interest on the selected gainimage.
 9. The system as set forth in claim 1 wherein the feature ofinterest is an ID and the processor includes an ID decoding process. 10.The system as set forth in claim 1 wherein the gain image is acquired ata minimal exposure time.
 11. The system as set forth in claim 1 whereinthe gain image is acquired using an exposure time of approximately 1millisecond.
 12. A method for auto-regulation of settings in a visionsystem camera acquiring images of scenes containing features ofinterest, the method comprising the steps of: receiving a trigger signalthat requests image acquisition of images from an image sensor having anarray of pixels arranged in discrete pixel groups; applying a gainsetting to at least one image acquired by the image sensor to produce atleast one gain image; determining, using a processor, whether the atleast one gain image is sufficiently readable to resolve information infeatures of interest and, if not, determining whether a change in thegain setting would improve quality of the image; and providing resultsto vary a predetermined parameter of the vision system.
 13. The methodas set forth in claim 12 wherein the predetermined parameter is at leastone of: intensity pattern and type of illumination.
 14. The method asset forth in claim 12 wherein the predetermined parameters is at leastone of (a) global gain and (b) exposure of the sensor.
 15. The method asset forth in claim 12 wherein the gain image is processed directly. 16.The method as set forth in claim 12 further comprising the step of,after determining whether the at least one gain image is sufficientlyreadable: computing new camera parameters, to apply to at least some ofthe pixels in the sensor to apply uniformly to at least some of thepixels of the image sensor, to produce a next image.
 17. The method asset forth in claim 16 wherein the camera parameters comprise at leastone of (a) global gain and (b) global exposure.
 18. The method as setforth in claim 12 further comprising the step of: obtaining a currentblack level reading from the sensor.
 19. The method as set forth inclaim 12 wherein each discrete pixel from each of the discrete pixelgroups is organized into each of a plurality of four-pixel matrices, thematrices being tessellated across the array of pixels.
 20. A system forauto-regulation of settings in a vision system camera acquiring imagesof scenes containing features of interest, the system comprising: meansfor receiving image data from an image sensor having an array of pixelsarranged in discrete pixel groups, each discrete pixel from each of thediscrete pixel groups is organized into each of a plurality of matrices,the matrices being tessellated across the array of pixels, that are eachindependently adjustable with respective gain setting; and means forselecting at least one acquired gain image associated with at least oneof the pixel groups, the selected gain image including a version of afeature of interest in an imaged scene that allows information to beread therefrom.
 21. The system as set forth in claim 20 wherein themeans for receiving image data and the means for selecting at least oneacquired gain image comprises a processor.